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DTSTART;TZID=Europe/Berlin:20230206T164500
DTEND;TZID=Europe/Berlin:20230206T174500
DTSTAMP:20230126T125829Z
CREATED:20221130T082641Z
LAST-MODIFIED:20230126T125829Z
UID:6913-1675701900-1675705500@alop.uni-trier.de
SUMMARY:ALOP Colloquium with Hanno Gottschalk
DESCRIPTION:On Monday\, February 6 2023 at 16:00 c.t. Prof. Dr. Hanno Gottschalk\, University of Wuppertal\, will speak at the ALOP Colloquium about his recent work: \n  \nTheory and Applications of Generative Adversarial Learning \nAbstract: \nGenerative learning is the machine learning task of learning to synthesize new examples that assimilate a given distribution. Since the introduction of generative adversarial networks in the year 2014\, generative learning underwent a rapid development and reached a considerable technological level. \nIn this talk\, generative adversarial learning is studied from a mathematical point of view. However\, we also give applications on domain transfer and sampling from the invariant measure of ergodic processes. \n  \nPlease join us at 16:45 c.t. in HS 10.
URL:https://alop.uni-trier.de/event/alop-colloquium-with-prof-dr-hanno-gottschalk-2/
LOCATION:Trier University\, E Building\, Universitätsring 15\, Trier\, Germany
CATEGORIES:Colloquium
ORGANIZER;CN="RTG ALOP at Trier University":MAILTO:ALOP@uni-trier.de
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DTSTART;TZID=Europe/Berlin:20230213T160000
DTEND;TZID=Europe/Berlin:20230213T170000
DTSTAMP:20230202T125328Z
CREATED:20221129T151056Z
LAST-MODIFIED:20230202T125328Z
UID:6897-1676304000-1676307600@alop.uni-trier.de
SUMMARY:ALOP Colloquium with Marc Goerigk
DESCRIPTION:On Monday\, February 13\, 2023 at 16:00 c.t. Prof. Dr. Marc Goerigk\, University of Siegen\, will speak at the ALOP Colloquium about his recent work: \n  \nModels for Interpretable Optimization \nAbstract: \nWhile explainability and interpretability has become a major research area in machine learning over the past years\, in operations research and mathematical programming the comprehensibility of solutions is hardly every scrutinized. Likely due to the existence of explicitly stated models and well defined solution processes\, experts have high confidence in the correctness and usefulness of found solutions. However\, even understanding a mathematical model can quickly become a challenging task\, especially for people not familiar with modeling techniques. Users with less mathematical and computer background\, e.g. the planner using the optimization software and the workers in charge of implementing the result\, may consider the solver a black box. We present and experimentally validate a modeling framework that inherently provides an interpretable decision rule\, trying to clarify the causal effect of occurring scenario and selected solution. By restricting the number of eligible solutions and providing an interpretable rule\, e.g. in form of a decision tree\, we shift the focus towards comprehensibility and transparency. Our experiments indicate that the costs of interpretability can be small\, i.e.\, only a small percentage of nominal performance needs to be sacrificed. \n  \nPlease join us at 16:00 c.t. in HS 9. \n 
URL:https://alop.uni-trier.de/event/alop-colloquium-with-marc-goerigk/
LOCATION:Trier University\, E Building\, Universitätsring 15\, Trier\, Germany
CATEGORIES:Colloquium
ORGANIZER;CN="RTG ALOP at Trier University":MAILTO:ALOP@uni-trier.de
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